Solving TDOA Problems in Communications Using Genetic Algorithms

This project implements a genetic algorithm solution for Time Difference of Arrival (TDOA) problems in communication systems. The algorithm consists of multiple modular components with specific functions: File: program Function: Controls multiple test iterations (account_test trials), extracts the optimal chromosome from each trial, and calculates performance metrics including mean value (MV) and mean squared error (MSE). The implementation follows an iterative optimization approach where each trial evolves chromosomes through selection, crossover, and mutation operations. File: definition_constant() Function: Defines and configures constant experimental parameters. This typically includes genetic algorithm parameters (population size, mutation rate, crossover rate), TDOA system parameters (base station coordinates, signal propagation speed), and noise characteristics. The function serves as a centralized configuration module for reproducible experiments. File: main_program Function: Executes computational logic for a single complete trial. This module coordinates the genetic algorithm workflow including population initialization, fitness evaluation based on TDOA measurements, and evolutionary operations until convergence criteria are met. File: all_Noise Function: Computes TDOA values affected by measurement noise from base stations. The implementation models realistic signal propagation conditions by adding Gaussian noise to ideal TDOA measurements, simulating real-world environmental interference in communication systems. File: gen_ini_pop_arr Function: Generates the initial chromosome matrix pop_arr for genetic algorithm operations. The matrix structure organizes genetic information with rows 1-2 representing estimated x and y coordinates (solution candidates), while rows 3-4 are initialized to zero for potential algorithm extensions. This population initialization uses random distribution within feasible solution bounds.